Location information is a fundamental context to be utilized to extract the geographical relationship between users and environments to further understand and learn the users' behaviors. The importance and promise of location-aware applications has led to the design and implementation of systems for providing location information. Currently, some high accuracy indoor positioning systems (Ha-IPSs) are developed to accurately track people and assets in real time, in many different application scenarios including office, healthcare, coalmine, subway, smart building, restaurant, and other environments.
Currently, these Ha-IPSs are typically ultrasound based or ultra-wideband-radio based. Their common character is to provide positioning accuracy of centimeter level. In some application scenarios of such Ha-IPSs, some positioning devices need be deployed and calibrated in the relevant environment in order to monitor the locations of moving objects in some Areas of Interest (AOI). Generally, positioning systems like Ha-IPSs can track the locations of these moving objects in real time so as to provide certain location-based service. For instance, in an office environment, when positioning devices such as Ha-IPSs are deployed, locations of terminals or employees can be tracked. Therefore, location-based access rules can be designed to define certain “secure zone.” Only within such a zone, access to confidential information databases can be allowed; beyond or out of the zone, any access will be prohibited. The above-noted secure zone may be a room, part of a working area, or even a table.
So far, varieties of Ha-IPSs have been developed to provide the geographical relationship between users and environments. In these Ha-IPSs, the positioning and geographical relationship determination process can be summarized as three phases.
1. Ha-IPS setting up phase, which can comprise the steps of:
1) calibrating the locations of reference points. The reference point locations refer to the locations of positioning devices or beacons when calculating the location of an object point, the locations of positioning devices or reference points must be known in advance and are used as calculation references in a positioning algorithm.
2) configuring the size of a reference space. The reference space means a space in which the object is moving, such as a room and an office. In order to learn the geographical relationship of the object to the environment, the size of the reference space must be known.
3) characterizing the Area of Interest. The Area of Interest (AOI) means a geographical area which is characterized by a user for some specific application requirements (such as for security purposes). The Area of Interest is located in the reference space. For example, in “Secure Table” application, the table is defined as the Area of Interest. Only within the Area of Interest, access to confidential information is allowed; beyond or out of the Area of Interest, any access to the confidential information is prohibited.
In the Ha-IPS setting up phase, since errors in reference point calibration will be inherited to the object positioning process, sufficiently accurate calibration is required. Additionally, since the positioning devices are commonly deployed on the ceiling, a calibration process with few human efforts is especially desired. Furthermore, since measuring a practical environment involves lots of human efforts, an accurate, fast and automatic reference space configuration method is especially desired.
2. Ha-IPS online locating phase. In this phase, the real-time location of the object point is calculated based on a measured distance of the object and the calibrated reference points' coordinates.
3. Geographical relationship inferring phase. In this phase, the object's geographical relation to the reference space and Areas of Interest is inferred based on the definitions of the reference space and Areas of Interest and the real-time location of the object point as calculated in the second phase. In this process, lots of measurement and recording efforts are spent because Areas of Interest are mainly characterized manually.
As discussed above, a common defect in the existing Ha-IPSs is that the configuration, calibration, and characterization thereof require enormous efforts. Hence, the use of the existing Ha-IPSs is not quite convenient, nor does it meet user-friendly requirements.
Therefore, there is a dire need in the art for a technical solution to automatically configure and calibrate a positioning device and also for a technical solution to automatically characterize an area of interest in a space.